提出跨硬件能效归一化方法，让模型能耗比较更公平。
Normalizing Energy Consumption for Hardware-Independent Evaluation
- 用两个参考点+浮点运算量和参数量来归一化能耗
- 在不同GPU上训练音频识别模型验证了方法有效性
- 适合关注模型环保性与能效评估的研究者
机器学习模型在信号处理中的广泛应用引发了对其环境影响的担忧，尤其是在资源密集型训练阶段。本文提出一种新型方法，对不同硬件平台上的能量消耗进行归一化，以实现公平、一致的比较。通过在不同GPU上训练多种机器学习架构并测量其在音频标记任务中的能耗，评估了不同的归一化策略。研究发现，参考点数量、回归类型以及计算指标的引入显著影响归一化效果。适当选择两个参考点可实现稳健的归一化，而引入浮点运算次数和参数量则能提高能耗预测的准确性。该方法有助于提升能源消耗评估的精确性，推动环境友好型机器学习实践的发展。
原文摘要 · Abstract (English)
The increasing use of machine learning (ML) models in signal processing has raised concerns about their environmental impact, particularly during resource-intensive training phases. In this study, we present a novel methodology for normalizing energy consumption across different hardware platforms to facilitate fair and consistent comparisons. We evaluate different normalization strategies by measuring the energy used to train different ML architectures on different GPUs, focusing on audio tagging tasks. Our approach shows that the number of reference points, the type of regression and the inclusion of computational metrics significantly influences the normalization process. We find that the appropriate selection of two reference points provides robust normalization, while incorporating the number of floating-point operations and parameters improves the accuracy of energy consumption predictions. By supporting more accurate energy consumption evaluation, our methodology promotes the development of environmentally sustainable ML practices.
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